Office Hours at SciPy 2015

Edit: 2015/7/1: Sessions will now be held each day of the conference during the afternoon coffee breaks from 3 – 3:30 PM.

Edit: 2015/7/2: Sessions will be held in room 210 on the main level of the conference center.

This year at SciPy 2015 I’d like to run some informal “office hours” help sessions to help people with any questions they might have. I can imagine questions about:

  • scientific Python libraries (NumPy, SciPy, Pandas, matplotlib…)
  • software installation (Anaconda, conda, pip…)
  • software packaging
  • Git & GitHub
  • the command line (shell)
  • web applications
  • much more!

The sessions will be during the afternoon coffee breaks 3 – 3:30 PM each day of the conference (Wednesday – Friday). The SciPy organizers have very kindly reserved room 210 for the sessions. Follow me on Twitter for any last minute updates.

If there seems to be significant interest I’ll try to find times for some additional sessions, but that might be hard to do.

Whether you’ve got questions or answers, I hope you’ll join!

Office Hours at SciPy 2015

SciPy 2015 Talk Help

In the interest of helping to improve the diversity and beginner friendliness of the SciPy conference, I’m offering to help first-time speakers from underrepresented groups with their talk proposals and potential talk preparations for SciPy 2015. If that sounds like you and you’d like my help editing a proposal and/or preparing a talk, send me an email.

Notes:

  • The deadline for proposals is April 1
  • The conference is July 8-10 in Austin, Texas
  • SciPy has a Code of Conduct
  • SciPy is committed to diversity
  • SciPy has some financial aid
  • I will be at the conference
  • I’m not a conference organizer, but I have in the past helped with talk selection (and may again this year)
  • I have never given a talk at SciPy (except lightning talks)

P.S. If you’re looking for some talk ideas, try this post.

SciPy 2015 Talk Help

SciPy 2015 Talk Ideas

The SciPy 2015 call for proposals is open until April 1. In case anyone wants to give a talk but doesn’t have an idea I came up with a few:

  • introduction to testing with a focus on numerics
  • guide to profiling
  • introduction to packaging and distribution
  • scidb
  • xray
  • bcolz
  • which tool to use for which job (cover core packages)
  • data visualization options
  • write a numpy ufunc in Python, Cython, and C
  • roundup of high-performance options (C, Cython, Numba, Parakeet, etc.)

Thanks to Rob Story for some suggestions. If you’ve got ideas for talks you’d like to see, leave a comment!

(I will be at SciPy 2015, but I’m organizing a Software Carpentry tutorial so I probably won’t be submitting a talk proposal.)

P.S. If you’re a first-time speaker from an underrepresented group thinking about giving a talk at SciPy 2015, I’m offering to help with proposal editing and talk prep.

SciPy 2015 Talk Ideas

If You Want to Build the NumPy and SciPy Docs

This week docs.scipy.org has been down, but folks still need their NumPy and SciPy docs. To fill the gap until docs.scipy.org is back up I built the docs for only the latest stable releases and uploaded them to GitHub pages:

How to Build

(Note that I’m working on a Mac and these instructions are a little Mac/Linux oriented. The procedure on Windows would not be drastically different, though.)

Continue reading “If You Want to Build the NumPy and SciPy Docs”

If You Want to Build the NumPy and SciPy Docs

Install Scientific Python on Mac OS X

These instructions detail how I install the scientific Python stack on my Mac. You can always check the Install Python page for other installation options.

I’m running the latest OS X Mountain Lion (10.8) but I think these instructions should work back to Snow Leopard (10.6). These instructions differ from my previous set primarily in that I now use Homebrew to install NumPy, SciPy, and matplotlib. I do this because Homebrew makes it easier to compile these with non-standard options that work around an issue with SciPy on OS X.

I’ll show how I install Python and the basic scientific Python stack:

If you need other libraries they can most likely be installed via pip and any dependencies can probably be installed via Homebrew.

Command Line Tools

The first order of business is to install the Apple command line tools. These include important things like development headers, gcc, and git. Head over to developer.apple.com/downloads, register for a free account, and download (then install) the latest “Command Line Tools for Xcode” for your version of OS X.

If you’ve already installed Xcode on Lion or Mountain Lion then you can install the command line tools from the preferences. If you’ve installed Xcode on Snow Leopard then you already have the command line tools.

Homebrew

Homebrew is my favorite package manager for OS X. It builds packages from source, intelligently re-uses libraries that are already part of OS X, and encourages best practices like installing Python packages with pip.

To install Homebrew paste the following in a terminal:

ruby -e "$(curl -fsSL https://raw.github.com/mxcl/homebrew/go)"

The brew command and any executables it installs will go in the directory /usr/bin/local so you want to make sure that goes at the front of your system’s PATH. As long as you’re at it, you can also add the directory where Python scripts get installed. Add the following line to your .profile, .bash_profile, or .bashrc file:

export PATH=/usr/local/bin:/usr/local/share/python:$PATH

At this point you should close your terminal and open a new one so that this PATH setting is in effect for the rest of the installation.

Python

Now you can use brew to install Python:

brew install python

Afterwards you should be able to run the commands

which python
which pip

and see

/usr/local/bin/python
/usr/local/bin/pip

for each, respectively. (It’s also possible to install Python 3 using Homebrew: brew install python3.)

NumPy

It is possible to use pip to install NumPy, but I use a Homebrew recipe so I avoid some problems with SciPy. The recipe isn’t included in stock Homebrew though, it requires “tapping” two other sources of Homebrew formula:

brew tap homebrew/science
brew tap samueljohn/python

You can learn more about these at their respective repositories:

With those repos tapped you can almost install NumPy, but first you’ll have
to use pip to install nose:

pip install nose

I compile NumPy against OpenBLAS to avoid a SciPy issue. Compiling OpenBLAS requires gfortran, which you can get via Homebrew:

brew install gfortran
brew install numpy --with-openblas

SciPy

And then you’re ready for SciPy:

brew install scipy --with-openblas

matplotlib

matplotlib generally installs just fine via pip but the custom Homebrew formula takes care of installing optional dependencies too:

brew install matplotlib

IPython

You’ll want Notebook support with IPython and that requires some extra dependencies, including ZeroMQ via brew:

brew install zeromq
pip install jinja2
pip install tornado
pip install pyzmq
pip install ipython

pandas

Pandas should install via pip:

pip install pandas

Testing It Out

The most basic test you can do to make sure everything worked is open up an IPython session and type in the following:

import numpy
import scipy
import matplotlib
import pandas

If there are no errors then you’re ready to get started! Congratulations and enjoy!

Install Scientific Python on Mac OS X

IDL’s GAUSSFIT in Python

A colleague recently asked for help getting the functionality of IDL’s GAUSSFIT function working in Python. This was a perfect opportunity to use the handy curve_fit function from SciPy. Here’s the code:

import numpy as np
from scipy.optimize import curve_fit

xdata, ydata = np.loadtxt('focus_output.dat', unpack=True)

def fit_func(x, a0, a1, a2, a3, a4, a5):
    z = (x - a1) / a2
    y = a0 * np.exp(-z**2 / a2) + a3 + a4 * x + a5 * x**2
    return y

parameters, covariance = curve_fit(fit_func, xdata, ydata)

The file focus_output.dat just contains some data in two columns of numbers. For more info on loadtxt see my post on reading text tables. fit_func defines the function we want to fit to the data. In this case it is a Gaussian plus a quadratic, the same as used in GAUSSFIT when NTERMS=6. Now, to plot the results:

import matplotlib.pyplot as plt

fitdata = fit_func(xdata, *parameters)

fig = plt.figure(figsize=(6,4), frameon=False)
ax = fig.add_axes([0, 0, 1, 1], axisbg='k')

ax.plot(xdata, ydata, 'c-', xdata, fitdata, 'm-', linewidth=3)

ax.set_ylim(0.38, 1.02)

fig.savefig('gauss_fit_demo.png')

Data and Fitted Function
Cyan shows the original data, magenta shows the function fit with parameters returned by curve_fit.
IDL’s GAUSSFIT in Python

Install Python, NumPy, SciPy, and matplotlib on Mac OS X – Double Click

Update: These instructions are over a year old, though they may still work for you. See the “Install Python” page for the most up-to-date instructions.

I’ve already written a post about installing Python, NumPy, SciPy, and matplotlib on Lion, but it involves a lot of working at the command line, modifying your .bash_profile and dealing with compiler problems. That’s what I’ll call the compile-it-yourself (CIY) method. What I’ll describe below I’ll call the “double click” method.

I personally use the CIY method because it allows me to very easily control what’s installed. With Homebrew and pip I can uninstall and upgrade different things at will, or choose to install bleeding-edge versions. But it’s more hassle than everyone wants and there’s now an easier way using double-click installers.

Until recently the CIY was the only way to get everything working on Lion but now the developers of NumPy, SciPy, and matplotlib have all caught up and it’s possible to just download and double-click on a few DMG files to get a basic scientific Python installation working. Once you get to know Python, though, you will undoubtedly want to install some other packages and when that time comes I suggest you use pip.

Continue reading “Install Python, NumPy, SciPy, and matplotlib on Mac OS X – Double Click”

Install Python, NumPy, SciPy, and matplotlib on Mac OS X – Double Click